Nikhil Ahuja

This research pioneers a novel method using open-source large language models to streamline natural language to SQL queries. Through Retrieval-Augmented Generation, it accurately identifies specific datasets and variables. It comprehends user queries, refines prompts, and aggregates data for precise SQL query generation, navigating complex dataset structures and diverse queries. Its adaptability handles varying dataset periods and fosters continued conversations, catering to specific analytical needs. Rigorous validation confirms its accuracy, offering a scalable, adaptable tool for managing complex databases. This framework marks a pivotal advancement, facilitating intuitive and efficient data interactions in just a hundred words.